class_docstr: PyTorch Lightning trainer for cnntransformer.
  
  Args:
      feature_extractor (torch.nn.Sequential, optional): the feature extractor network.
      optimizer (dict): optimizer parameters.
      lr_milestones (list): list of epoch indices. Must be increasing.
      lr_gamma (float): multiplicative factor of learning rate decay.
docstr: Set up an SGD optimizer and multistep learning rate scheduler. When self._adapt_lr is True, the learning
  rate will be decayed by self.lr_gamma every step in milestones.
docstr_tok: ["Set","up","an","SGD","optimizer","and","multistep","learning","rate","scheduler",".","When","self._adapt_lr","is","True",",","the","learning","rate","will","be","decayed","by","self.lr_gamma","every","step","in","milestones","."]
code: def configure_optimizers(self):
      """Set up an SGD optimizer and multistep learning rate scheduler. When self._adapt_lr is True, the learning
      rate will be decayed by self.lr_gamma every step in milestones.
      """
      optimizer = torch.optim.SGD(
          self.parameters(),
          lr=self._init_lr,
          **self._optimizer_params["optim_params"],
      )
      if self._adapt_lr:
          scheduler = torch.optim.lr_scheduler.MultiStepLR(
              optimizer,
              milestones=self.lr_milestones,
              gamma=self.lr_gamma,
          )
          return [optimizer], [scheduler]
      return [optimizer]
  
imports: import pytorch_lightning
  import torch
  import torch.nn
  from sklearn.metrics import accuracy_score
  from kale.predict import losses
label_desc: Use image node_id 4 for calling the CNNTransformerTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list, list,  list